• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.523-535
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• 2008

Initial work of microarray data analysis focused on identification of differentially expressed genes, and recently, the focus has moved to discovering significant sets of functionally related genes. We describe some problems of GSEA and PAGE, and propose a modified method to identify significant gene sets. The results based on a simulated experiment and real data analysis using a set of publicly available data show the superiority of the newly proposed method, GSA-AT, in detecting significant pathways with the accurate prediction.

• Proceedings of the Korean Society of Life Science Conference
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• 2000.06a
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• pp.3-8
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• 2000

The complete genome sequence of a Gram-positive bacterium .Bacillus subtilis has recently been reported and it is now clear that more than 50% of its ORFs have no known function (1). To study the global gene expression in B. subtilis at single gene resolution, we have tested the glass DNA microarrays in a step-wise fashion. As a preliminary experiment, we have created arrays of PCR products for 14 ORF whose transcription patterns have been well established through transcriptional mapping analysis. We measured changes in mRNA transcript levels between early exponential and stationary phase by hybridizing fluorescently labeled cDNA (with Cy3-UTP and Cy5-UTP) onto the array. We then compared the microarray data to confirm that the transcription patterns of these genes are well consistent with the known Northern analysis data. Since the preliminary test has been successful, we scaled up the experiments to ${\sim}$94% of the 4,100 annotated ORFs for the complete genome sequence of B. subtilis. Using this whole genomic microarray, we searched genes that are catabolite-repressive and those that are under the control of ${\sigma}^{Y}$, one of the functionally unknown ECF sigma factors. From these results, we here report that we have established DNA microarray techniques that are applicable for the whole genome of B. subtilis.

• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• Journal of Life Science
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• v.24 no.8
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• pp.843-850
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• 2014

In the current study, we prepared eighty-five different kinds of solvent fractions of rice wine lees and nuruk extracts and investigated their effects on cell viability and nitric oxide (NO) production in mouse RAW 264.7 cells. Among the treated solvent fractions, only three solvent fractions (KSD-E1-3, KSD-E2-3 and KSD-E4-3) significantly decreased NO production in LPS-activated RAW 264.7 cells without affecting cell viability. And, they also reduced the expression of pro-inflammatory genes such as COX-2, TNF-alpha and iNOS. To understand the molecular mechanisms involved in the inhibition of inflammation in (KSD-E4-3)-treated RAW 264.7 cells, we carried out oligo DNA microarray analysis using Agilent Mouse microarray. To confirm microarray data, 6 genes (IL-1F6, iNOS, IL-10, Fabp4, IL-1RN and CSF2) were selected and performed RT-PCR and quantitative real-time PCR analysis with gene specific primers. The results of RT-PCR and real-time PCR agreed with microarray data. Overall, our results suggest that rice wine lees can be a novel resource for the development of foods and drugs which possess anti-inflammatory activity.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.397-408
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• 2009

The development of microarray technology makes possible to analyse many thousands of genes simultaneously. While it is important to test each gene whether it shows changes in expression associated with a phenotype, human diseases are thought to occur through the interactions of multiple genes within a same functional cafe-gory. Recent research interests aims to directly test the behavior of sets of functionally related genes, instead of focusing on single genes. Gene set enrichment analysis(GSEA), significance analysis of microarray to gene-set analysis(SAM-GS) and parametric analysis of gene set enrichment(PAGE) have been applied widely as a tool for gene-set analyses. We describe their problems and propose an alternative method using a parametric analysis by adopting normal score transformation of gene expression values. Performance of the newly derived method is compared with previous methods on three real microarray datasets.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.173-173
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• 2003

With the introduction of DNA microarrays, a high throughput analysis of gene expression is now possible as a replacement to the traditional time-consuming Southern-blot analysis. This cDNA microarray should be ahighly favored technology in the area of molecular toxicology or analysis of environmental stresses.In this study, therefore, we developed a novel cDNA microarray for analyzing stress-specific responses in japanese Medaka fish. In the design and fabrication of this stress specific functional cDNA microarray, 123 different genes in Medaka fish were selected from eighteen different stress responsive groups and spotted on a 25${\times}$75 mm glass surface. After exposure of the fish to bisphenol A which is the one of the well-known endocrine disrupting chemicals (EDCs), over 1 or 10 days, the responses of the DNA chip were found to show distinct expression patterns according to the mode of toxic actions from environmental toxicants. As a results, they showed specific gene expression pattern to bisphenol A, additionally, the chemical spesific biomarkers could be suggested based on the chip analysis data. Therefore, this chip can be used to monitor stress responses of unknown and/or known toxic chemicals using Medaka fish and may be used for the further development of biomarkers by utilizing the gene expression patterns for known contaminants.

• Journal of Nutrition and Health
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• v.35 no.10
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• pp.1053-1059
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• 2002

In post-genome period, the technique for identifying gene expression has been changed to high throughput screening. In the field of molecular nutrition, the need for this technique to clarify molecular function of the specific nutrient is essential. In this study, we have tested the zinc-regulated gene expression in zinc-deficient U937 cells, using cDNA microarray which is the cutting-edge technique to screen large numbers of gene expression simultaneously. The study result can be used for the preliminary gene screening data for clarifying, using monocyte U937 cell line, molecular Zn aspect in atherosclerosis. U937 cells were cultured in Zn-adequate (control, 12 $\mu$M Zn) or Zn-deficient (experimental, 0 $\mu$M Zn) ESMI media during 2 days, respectively. Cells were harvested and RNA was extracted. Total RNA was reverse-transcriptinized and synthesized cDNA probe labeled with Cy-3. fluorescent labeled cDNA probe was applied to microarray slide for hybridization slide, and after then, the slide was scanned using fluorescence scanner. ‘Highly expressed genes’ in Zn-deficient U937 cells, comparing to Zn-adequate group, are mainly about the genes for motility protein, immune system protein, oncogene and tumor suppressor and ‘Less highly expressed genes’ are about the genes for transcription, apoptosis associated protein, cell cycle, and several basic transcription factors. The results of this preliminary study imply the effectiveness of cDNA microarray for expression profiling of a singly nutrient deficiency, specially Zn. Furthur study, using tailored-cDNA array and capillary endothelial cell lines, would be beneficial to clarify molecular Zn function, more in detail.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.131-140
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• 2010

Biclustering is a method of finding meaningful subsets of objects and attributes simultaneously, which may not be detected by traditional clustering methods. It is popularly used for the analysis of microarray data representing the expression levels of genes by conditions. Usually, biclustering algorithms do not consider a sequential relation between attributes. For time series data, however, bicluster solutions should keep the time sequence. This paper proposes a new biclustering algorithm for time series data by modifying the plaid model. The proposed algorithm introduces a parameter controlling an interval between two selected time points. Also, the pruning step preventing an over-fitting problem is modified so as to eliminate only starting or ending points. Results from artificial data sets show that the proposed method is more suitable for the extraction of biclusters from time series data sets. Moreover, by using the proposed method, we find some interesting observations from real-world time-course microarray data sets and apartment price data sets in metropolitan areas.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1172-1180
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• 2004

Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.